Function to compute 3D gradient with unevenly spaced sample locations

Question

I have experimental observations in a volume:

import numpy as np

# observations are not uniformly spaced 
x = np.random.normal(0, 1, 10)
y = np.random.normal(5, 2, 10)
z = np.random.normal(10, 3, 10)
xx, yy, zz = np.meshgrid(x, y, z, indexing='ij')

# fake temperatures at those coords
tt = xx*2 + yy*2 + zz*2

# sample distances
dx = np.diff(x)
dy = np.diff(y)
dz = np.diff(z)

grad = np.gradient(tt, [dx, dy, dz])  # returns error

This gives me the error:

ValueError: operands could not be broadcast together with shapes (10,10,10) (3,9) (10,10,10) .

EDIT: according to @jay-kominek in the comments below:

np.gradient won't work for you, it simply doesn't handle unevenly sampled data.

I've updated the question. Is there any function which can can do my computation?

Answer 1

Two things to note: First, scalars are single values, not arrays. Second, the signature of the function is numpy.gradient(f, *varargs, **kwargs) . Note the * before varargs . That means if varargs is a list, you pass *varargs . Or you can just provide the elements of varargs as separate arguments.

So, np.gradient wants a single value for the distance along each dimension, like:

np.gradient(tt, np.diff(x)[0], np.diff(y)[0], np.diff(z)[0])

or:

distances = [np.diff(x)[0], np.diff(y)[0], np.diff(z)[0]]
np.gradient(tt, *distances)

Answer 2

The required dx ... to be passed to np.gradient aren't grids of differences, but just one scalar each. So grad = np.gradient(tt,0.1,0.1,0.1) appears to work.